Problem

How do you get the cheapest rate possible when shipping products if you are managing billions of dollars in freight spend?

Most large shippers of goods send out a bunch of emails to different trucking companies with a spreadsheet of all the lanes (point A to point B) they are going to use to ship their products. They take the rate estimates and combine it all into one big file to analyze it as best as they can to "award" the freight to the cheapest option per lane.

Even if you're able to combine all those estimated rates into a single file, and you're smart enough to do the formulas in excel to figure out the cheapest cost possible, that's not how the world works.

Theres a million reasons a shipper can't use the company with cheapest rate, from insurance coverage to service and even company initiatives like using veteran owned businesses.

So how do you get the cheapest possible rates while also constrained by a bunch of other variables?

Ideation

Each user has vastly different needs, because their business is unique. But when you look at a grander scale, patterns start to emerge.

We were able to meet with a lot of users to figure out which variables needed to be accounted for, and they were all very different from business to business.

Something that came to mind as we collected data was the concept of desire paths. Essentially, in physical spaces you wait to put pathways in place until you know where people want to go. From above the paths might look strange, but to a user with a goal it makes perfect sense.

I started to take the goals from users stated in their own words, and would highlight parts of speech that were similar and began to find similar paths. This led to one consistent schema that accounted for a lot of different goals.

Example Pattern

I don't want brokers to win more than 50% of volume on each lane originating out of Indiana.

Here's a real life example of something a shipper wanted to do. After reviewing hundreds of examples I was able to map out almost all requests to a series of 5 pretty simple data points.

Goal

Allocate 50% or less of volume

Lane Filter

Lanes out of Indiana

Lane Target

Each lane individually

Participant Filter

Brokers

Participant Target

All brokers as a whole

Design & Implementation

Taking inspiration from physical wayfinding, I was able to guide users to defined paths and make them easily recognizable through iconography and defined nomenclature.

After identifying the main data points we needed to collect, I defined purpose built paths for each goal. If a user wants to allocate a specific amount of volume, we ask them follow up questions specific to that goal, making sure no one gets lost along the way.

I knew we needed the user to go through a decision tree that would change depending on the goal and there are great examples of UI's out there that accomplish this. But the hard part about the UX was making sure the decisions were clear to the user so they could follow allong properly. There are lots of industry terms and jargon that can be easily misconstrued so we established a defined nomenclature and iconography for each step and made sure we were consistent, like any good physical wayfinding or map.

Here's a video of me demoing the product

Results

After a lot of ideation and iteration, we released a freight optimization tool that met the needs of our most advanced users while still being simple enough for our most basic ones.

I knew making it a success would mean having all areas of our business aligned on how it worked and making sure customers had resources on how to use it. So I made interactive documentation inside of Figma that was used to train customer support reps and made public for all users.

The work done on this project directly contributed to signing contracts with some of the largest shippers of goods in the U.S.

So how do you get the cheapest possible rates while also constrained by a bunch of other variables?

Holden Hays

Showcase

Empowering everyday users to become freight masterminds